• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.339-345
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• 2004

This paper deals with a mixed logit model for ordered polytomous data. There are two types of factors affecting the response varable in this paper. One is a fixed factor with finite quantitative levels and the other is a random factor coming from an experimental structure such as a randomized complete block design. It is discussed how to set up the model for analyzing ordered polytomous data and illustrated how to estimate the paramers in the given model.

• Journal of Global Scholars of Marketing Science
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• v.15 no.2
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• pp.141-159
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• 2005

In this research, we develope an estimation method for the estimation of the market potential in the new service (or product) diffusion model. The developed method is based on the ordered response model which can effectively incorporate the survey result of the multi-point scale intention for subscription as well as the responder's characteristics, the characteristics & attitudes of the related service. We also apply the developed method to an estimation of the market potential of the digital multimedia broadcasting (DMB) service. As a result, an optimistic and a pessimistic estimates of DMB market potential are 41.10% and 14.83% of the cellular subscribers respectively.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.123-130
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• 2006

This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.121-126
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• 2004

This paper discusses about how to build up a mixed-effects model using cumulative logits when there are some factors are fixed and others are random. Random factors are assumed to be coming from a two-way nested design for choosing individuals or experimental units to apply treatments. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.187-193
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• 2006

This paper shows how to use continuation-ratio logits for the analysis of structured polytomous data. Here, response categories are considered to have a nested binary structure. Thus, conditionally nested binary random variables can be defined in each step. Two types of factors are considered as independent variables affecting response probabilities. For the purpose of analyzing categorical data with binary nested strutures a continuation-ratio mixed model is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed in detail by an example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.207-215
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• 1997

This paper deals with a comparison between Taguchi's accumulation analysis method and Nair test on the ordered categorical data from an industrial experiment for quality improvement. a result of Taguchi's accumulation analysis method is shown to have reasonable power for detecting location effects, while Nair test identifies the location and dispersion effects separately, Accordingly, Taguchi's accumulation analysis needs to develop methods for detecting dispersion effects as well as location effects. In addition this paper rewmmends models for analyzing ordered categorical data, for examples, the cumulative legit model, mean response model etc Successively simple, reasonable methods should be introduced more likely to be used by the practitioners.

• Journal of Korean Elementary Science Education
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• v.32 no.2
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• pp.139-158
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• 2013

This study investigated elementary students' (grade 4~6) learning progressions for water cycling drawn from iterative assessments using ordered multiple-choice (OMC) items. An assessment system, which consisted of construct map, item design, outcome space, and measurement model, was employed in this study to examine children's learning progressions. At the first stage of the assessment system, a construct map was designed on which children's conceptual understandings from naive to most sophisticated were represented. At the item design stage, 8 OMC items were drawn from the construct map. Each item option of the OMC items was scored from 0 to 3 according to its level of understanding at the stage of outcome space. As a measurement model, Rasch model, a branch of item response theory, was applied to interpreting the outcomes of the OMC items. This cycle of assessment system was furtherly implemented iteratively in order to elaborate on the first version of water cycling learning progression. In conclusion, children's understanding of water cycling could be described in two aspects: water distribution and water movement. We identified children's conjectural developmental pathways about water cycling existed from superficial and naive accounts to more complex and abstract accounts.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.471-479
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• 2007

This paper discusses about how to build up mixed-effects model for analysing ordinal response data by using cumulative logits. Random factors are assumed to be coming from the designed sampling scheme for choosing observational units. Since the observed responses of individuals are ordinal, a proportional odds model with two random effects is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.135-141
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• 2009

A variety of statistical methods are applied to model and optimize responses, related to product or system's quality, in terms of control and noise factors at design and manufacturing stages. Most of them assume continuous response variables but, assessing the performance of a product or system often involves categorical observations, such as ratings and scores. Although most previous works to deal with the categorical data provide sorhisticated response models and ensure unbiased outcomes, they require heavy computation to estimate the model parameters, as well as enough replications. In this study, we present some practical approaches for optimal parameter design with ordered categorical response when only a few or no replication is available. Two real-life examples are given to illustrate the presented methods.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.363-376
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• 2012

Excessive zeroes are often observed in ordinal categorical response variables. An ordinary ordered Probit model is not appropriate for zero-inflated data especially when there are many different sources of generating 0 observations. In this paper, we apply a two-stage zero-inflated ordered Probit (ZIOP) model which incorporate the zero-flated nature of data, propose a Bayesian analysis of a ZIOP model, and apply the method to alcohol consumption data collected by the National Bureau of Statistics, Korea. In the first stage of a ZIOP model, a Probit model is introduced to divide the non-drinkers into genuine non-drinkers who do not participate in drinking due to personal beliefs or permanent health problems and potential drinkers who did not drink at the time of the survey but have the potential to become drinkers. In the second stage, an ordered probit model is applied to drinkers that consists of zero-consumption potential drinkers and positive consumption drinkers. The analysis results show that about 30% of non-drinkers are genuine non-drinkers and hence the Korean alcohol consumption data has the feature of zero-inflated data. A study on the marginal effect of each explanatory variable shows that certain explanatory variables have effects on the genuine non-drinkers and potential drinkers in opposite directions, which may not be detected by an ordered Probit model.